College of Agricultural Engineering, Shanxi Agricultural University, Jinzhong 030800, China.
College of Information Science and Engineering, Shanxi Agricultural University, Jinzhong 030800, China.
Sensors (Basel). 2021 Dec 28;22(1):173. doi: 10.3390/s22010173.
The intelligent identification and classification of plant diseases is an important research objective in agriculture. In this study, in order to realize the rapid and accurate identification of apple leaf disease, a new lightweight convolutional neural network RegNet was proposed. A series of comparative experiments had been conducted based on 2141 images of 5 apple leaf diseases (rust, scab, ring rot, panonychus ulmi, and healthy leaves) in the field environment. To assess the effectiveness of the RegNet model, a series of comparison experiments were conducted with state-of-the-art convolutional neural networks (CNN) such as ShuffleNet, EfficientNet-B0, MobileNetV3, and Vision Transformer. The results show that RegNet-Adam with a learning rate of 0.0001 obtained an average accuracy of 99.8% on the validation set and an overall accuracy of 99.23% on the test set, outperforming all other pre-trained models. In other words, the proposed method based on transfer learning established in this research can realize the rapid and accurate identification of apple leaf disease.
植物病害的智能识别与分类是农业领域的一个重要研究目标。在这项研究中，为了实现苹果叶病害的快速准确识别，提出了一种新的轻量级卷积神经网络 RegNet。在田间环境下，对 5 种苹果叶病害（锈病、黑星病、轮纹病、苹果红蜘蛛和健康叶片）的 2141 张图像进行了一系列的对比实验。为了评估 RegNet 模型的有效性，与 ShuffleNet、EfficientNet-B0、MobileNetV3 和 Vision Transformer 等先进的卷积神经网络（CNN）进行了一系列的对比实验。结果表明，在验证集上，学习率为 0.0001 的 RegNet-Adam 获得了 99.8%的平均准确率，在测试集上的整体准确率为 99.23%，优于所有其他预训练模型。换句话说，本研究基于迁移学习建立的方法可以实现苹果叶病害的快速准确识别。
